A Vector Field Visualization Technique for Self-organizing Maps

Pölzlbauer, Georg; Rauber, Andreas; Dittenbach, Michael

doi:10.1007/11430919_47

Cited by 18 publications

(20 citation statements)

References 6 publications

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“… Comparison of quantization error needs to be under the same grid size. [18]. To determine the optimal grid size we use the approach by Vesanto [19]: G = 5 ∗ sqrt ( N ), where N is the size of the input data.…”

Section: Methodsmentioning

confidence: 99%

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data

Kang,

Szabo,

Farago

et al. 2024

Preprint

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Multiplexed imaging at single-cell resolution is becoming widely used to decipher the role of the cellular microenvironment in cancer and other complex diseases. To identify spatial patterns of single cells on a tissue, accurate cell-type phenotyping is a crucial step. This step is challenging due to (i) fluorescence noise and batch effects, (ii) segmentation artifacts, (iii) laborious annotation of ground truth, and (iv) difficulty in reproducing human-biased thresholding. Here we present Tribus, an interactive, knowledge-based classifier that avoids hard-set thresholds and manual labeling, is robust to noise, and takes fewer iterations from the user than current methods of labeling. Tribus has built-in visualization functions to gain insight into the input data and to evaluate the results. The Napari plug-in provides a user-friendly way to visualize the results and perform quality control. In this study, we validate the accuracy of Tribus by comparing its results to labels in public benchmarking datasets where manual cell type annotations are supported by the pathology community. We applied Tribus on a cyclic immunofluorescence (CyCIF) dataset, consisting of five matched ovarian cancer samples collected before and after neoadjuvant chemotherapy. Accurate cell-type phenotyping enabled a high-resolution analysis of cellular phenotypes, their spatial patterns, and their temporal dynamics during platinum-taxane chemotherapy. Tribus is provided as an easily embeddable open-source package and enables accurate phenotyping of single cells to facilitate biological discovery from highly multiplexed images.

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Section: Methodsmentioning

confidence: 99%

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data

Kang,

Szabo,

Farago

et al. 2024

Preprint

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show abstract

“…The training of a SOM is a stochastic approximation process, thus usually several maps need to be built and the best one is selected. A big variety of measures for the goodness of mapping has been proposed, , but there is still no uniformly recognized method to evaluate the quality of a particular SOM. Thus, in the following experiments with single structure representation ten SOMs were trained, and the obtained ranked lists were merged using eq 3.…”

Section: Methodsmentioning

confidence: 99%

Ligand-Based Virtual Screening by Novelty Detection with Self-Organizing Maps

Hristozov

Oprea

Gasteiger

2007

J. Chem. Inf. Model.

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We describe a novel method for ligand-based virtual screening, based on utilizing Self-Organizing Maps (SOM) as a novelty detection device. Novelty detection (or one-class classification) refers to the attempt of identifying patterns that do not belong to the space covered by a given data set. In ligand-based virtual screening, chemical structures perceived as novel lie outside the known activity space and can therefore be discarded from further investigation. In this context, the concept of "novel structure" refers to a compound, which is unlikely to share the activity of the query structures. Compounds not perceived as "novel" are suspected to share the activity of the query structures. Nowadays, various databases contain active structures but access to compounds which have been found to be inactive in a biological assay is limited. This work addresses this problem via novelty detection, which does not require proven inactive compounds. The structures are described by spatial autocorrelation functions weighted by atomic physicochemical properties. Different methods for selecting a subset of targets from a larger set are discussed. A comparison with similarity search based on Daylight fingerprints followed by data fusion is presented. The two methods complement each other to a large extent. In a retrospective screening of the WOMBAT database novelty detection with SOM gave enrichment factors between 105 and 462-an improvement over the similarity search based on Daylight fingerprints between 25% and 100%, when the 100 top ranked structures were considered. Novelty detection with SOM is applicable (1) to improve the retrieval of potentially active compounds also in concert with other virtual screening methods; (2) as a library design tool for discarding a large number of compounds, which are unlikely to possess a given biological activity; and (3) for selecting a small number of potentially active compounds from a large data set.

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“…The sensitivity tests were based on the quantization error (QE), measuring how much detail is being learned by the SOMs, and on the topological error (TE), measuring the properties of the preserved space and the variation percentage of each pattern. This empirical procedure depends intrinsically on the study (Polzlbauer, 2004). We determined the optimal number of patterns by quantifying the associated QE and TE errors through various tests using different cluster arrangements, including 2 × 2, 2 × 3, 3 × 3, 3 × 4, and 4 × 4.…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Lagrangian Characterization of Surface Transport From the Equatorial Atlantic to the Caribbean Sea Using Climatological Lagrangian Coherent Structures and Self‐Organizing Maps

et al. 2023

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This study presents an assessment of the transport of suspended material by surface ocean currents, which have a critical role in determining the connectivity and distribution of living and non‐living material. Lagrangian experiments reveal pathways from the Equatorial Atlantic to 10 strategic regions within the Caribbean Sea, determined by considering the space‐time variability of climatological Lagrangian Coherent Structures, which act as recurrent attracting pathways and transport barriers. Due to windage or Stokes drift, wind forcing is a significant factor in determining the spatial locations where particles cluster and the time needed to reach the Caribbean from the Equatorial Atlantic. Pathways shift westward within the Caribbean and take less time to arrive with increasing wind influence. Depending on the wind effect, the particles show higher confluence in different areas of the Caribbean. A case study is presented for the Mexican Caribbean nearshore area, isolated from ocean‐current trajectories. Here, wind weakens the transport barrier responsible for this isolation and causes particle confluence toward that region. Spatial patterns of the Eulerian velocity identified through Self‐Organizing Maps, with time dependence given by their best matching units, can reproduce the characteristic Lagrangian patterns of surface current climate variability. Our study demonstrates the application of tools from dynamical systems and unsupervised neural networks to understand Lagrangian patterns and identify the processes that drive them. These findings improve our understanding of transport mechanisms of suspended material by surface ocean currents in the Western Atlantic and the Caribbean Sea, which is essential for managing and conserving marine ecosystems.

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A Vector Field Visualization Technique for Self-organizing Maps

Cited by 18 publications

References 6 publications

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data

Ligand-Based Virtual Screening by Novelty Detection with Self-Organizing Maps

Lagrangian Characterization of Surface Transport From the Equatorial Atlantic to the Caribbean Sea Using Climatological Lagrangian Coherent Structures and Self‐Organizing Maps

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